Method and system for determining package integrity

ABSTRACT

Methods and systems for detecting and monitoring defects in a sealing region of a container, including imaging at least part of the sealing region of the container using a camera operative at a wavelength in the range of 0.76 μm-14 μm; during and/or after the filling of the container with a filling material and prior to sealing of the container being completed; and identifying, based on at least one frame obtained from the imaging, defects in the sealing region.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is Continuation in Part of U.S. patent application Ser.No. 16/485,533 filed on Aug. 13, 2019, which is a National Phase of PCTPatent Application No. PCT/IL2018/050114 having International filingdate of Feb. 1, 2018, which claims the benefit of priority of U.S.Provisional Application No. 62/461,026 filed on Feb. 20, 2017. Thecontents of the above applications are all incorporated by reference asif fully set forth herein in their entirety.

FIELD OF THE INVENTION

This invention relates to a methods and systems for determining packageintegrity, based on imaging of the package filling.

BACKGROUND OF THE INVENTION

The integrity of a packaged product is critical for maintaining productquality until it reaches the end user. Defects in hermeticity of apackage may cause contamination, introduction of moisture etc., whichmay result in loss of quality and even pose a safety hazard. It istherefore important to ensure the integrity of the packaged products atleast at the end of their production process.

Currently, there are various techniques to verify the integrity of aproduced package. Most typically the evaluation is based on sporadictesting by physical examination.

U.S. Pat. No. 5,029,463 et al. describes a leak detection device forin-line measurement of package integrity.

U.S. Pat. No. 5,150,605 describes a method for determining propersealing of a hermetically sealed package.

U.S. Pat. No. 7,434,372 et al. describes a package integrity testcomprising, inter alia, pressurizing the package with a gas andmonitoring pressure to determine package integrity.

U.S. Pat. No. 7,565,828 describes apparatus and methods for testingpackage integrity and/or seal strength.

EP 0 355 699 describes a method for inspecting leakage of a sealedcontainer. The method comprises changing an internal pressure of avacuum chamber provided therein with an eddy-current displacement sensorto a predetermined degree of vacuum from a normal pressure after puttinga sealed container having a conductive material at least at a portion tobe inspected in the chamber; detecting an amount of expansion of thesealed container at the degree of vacuum in time sequence by theeddy-current displacement sensor; and determining any aging change inthe amount of expansion after a time when the detected amount ofexpansion shows the maximum value, thereby to find out any pin holeformed in the sealed container.

WO 2014/195943 describes a method and system for determining integrityof a product. The method comprises; (a) placing the product between atleast one radiation emitting body and one infra-red sensing arrangementcomprising at least one IR sensor, the product comprises a housing beingessentially transparent to IR radiation; (b) while the product is at asteady state temperature which is different from the temperature of theradiation emitting body, creating a sensing session comprising sensingby the at least one IR sensor, radiation emitted from the radiationemitting body, at least a portion of the emitted radiation beingtransmitted through the housing of the product, and (c) generating IRdata from the sensed radiation, the IR data being indicative of theintegrity of the product; wherein the product is spaced apart from atleast the radiation emitting body such that no contact exists therebetween.

SUMMARY OF THE INVENTION

The present invention provides utilizing thermal imaging for determiningpackage integrity of packed products to assure proper sealing of thepackage while the thermal image is being taken:

1. Phase A: during or after filling of the package (but prior to sealingphase completion).

2. Phase B: after sealing phase completion.

3. During both, phase A and phase B

Complete and lasting sealing is a critical stage of most packagingprocesses, and sealing integrity needs to be inspected/tested in orderto avoid messy leaks, costly product returns, damage to the productitself and/or damage to brand reputation. Packaging lines typically runat a fast pace, making traditional leak testing methods, such as avacuum or pressure decay testing, or squeezing too slowly, tooexpensive, and impractical. Moreover, these leak testing methods arebased on statistical sampling and typically enable monitoring thesealing process itself (i.e. temperature applied), but most often thesetests are incapable of detecting improper sealing caused by defects suchas contamination of the sealing region by package content during fillingof the package.

Thermal imaging (also known by the term “thermographic imaging”) is atype of infrared (IR) imaging in which radiation emitted from asubstance is detected based on the temperature and emissivity at one ormore locations across the substance (according to Black Body radiationlaw), and IR images are produced according to the detected temperaturesand emissivity. Typically, the amount of radiation emitted by asubstance increases with temperature. Therefore, thermography allowsdetecting variations in temperature and/or emissivity of a substance.For example, when viewed by a thermographic camera, warm objects can bedifferentiated from cooler backgrounds. Similarly, because ofdifferences in emissivity, liquid-based materials (including liquids,creams, pastes, foams, etc.) can be differentiated from dry products(e.g. the packaging material) using thermal imaging.

The inventors of the present invention have surprisingly found thatdefects in the sealing of packages can be detected by thermal imaging atthe filling phase, i.e. before the heat sealing of the package. This isparticularly advantageous for packages made of materials having athermal imaging signature, which is not sensitive enough to identifycontamination or other defects of the sealing region, when heated.

This problem is common to most heat-sealed products, in particulartubes, such as tubes used for packaging of cosmetics, toothpaste,pharmaceuticals, food, and the like. This is because sealing isperformed on two thick layers of material (e.g. plastic,plastic-aluminum, aluminum, and the like), having a high heat capacity,which make identifying changes in heat capacity due to contamination orother defects in the sealing region difficult.

Moreover, since the material filling the tubes is typically in a liquid,paste or cream form, it tends to splash during the filling phase andcontaminate the sealing region. These splashes are for the most partclear, colorless, or having a similar color as its container, making ita hard task for standard visual imaging.

Surprisingly, it was found by the inventors of the present application,that such non-solid materials (e.g. liquid, paste, foams or creams),have a different emissivity and thermal signature than their surroundingcontainer, thus enabling reliable detection via thermal imaging. This isbecause of the increase in signal contrast and decrease in reflectionnoise, as compared to standard visual imaging.

Accordingly, there is provided herein a system and method foridentification of improper sealing, by monitoring the sealing region byimaging during the filling phase of the packaging process. According tosome embodiments, the imaging is thermal imaging performed in awavelength range of 3 μm-14 μm, preferably in a wavelength range of 8μm-14 μm. According to some embodiments, the method identification ofdefects in or on the sealing region prior to completion of the sealingpackages. Non-limiting examples of defects that may be identifiedinclude contamination, a cut, a deformation, a bulk an uneventemperature or any combination thereof. According to some embodiments,the method enables real time identification of individual contaminationevents of the sealing regions of packages. According to someembodiments, the identification of the defect (e.g. contamination of thesealing region by filling material) is performed during the fillingphase, prior to and/or in addition to, monitoring sealing integrityduring the sealing of the packaging process. As a result, the disclosedsystem and method enables preventing the advancement of a package with adefect e.g. contaminated sealing region to a sealing station of thepackaging process and will thus reduce risk of low quality sealedpackages exiting the packaging line. Additionally or alternatively, thedisclosed system and method enables identification of a package with adefect e.g. a contaminated sealing region reaching a sealing station ofthe packaging process and will prevent distribution of improperly sealedpackages.

According to some embodiments, the sealing region may be heated orcooled (e.g. using air blowers) prior or after the filling phase so asto increase the gradient between the radiation emitted from the sealingregion material and the radiation emitted by the product (also referredto herein as “filling material”—e.g. a cosmetic cream). This maycontribute to reliable detection of the defects particularlycontamination in the sealing region, before the package enters the heatsealing station.

Heating or cooling the filling material (e.g. tooth paste) before orduring the filling phase to increase the gradient between the radiationemitted from the package in the sealing region and the radiation emittedfrom the filling material (e.g. cosmetic cream) will improve thedetection of the defects, particularly contamination in the sealingregion before the package enters the heat sealing station.

According to some embodiments, the system and method disclosed hereinmay enable applying, optionally simultaneously, both thermal imaging andstandard visual (VIS) imaging and applying image processing on bothimages to improve the reliability of the results.

According to some embodiments, the system and method disclosed hereinmay enable applying, optionally, both thermal imaging and standardvisual (VIS) imaging and applying image processing on both images toimprove the reliability of the results (e.g. visual imaging after orduring the filling phase and thermal imaging after the sealing phase).

According to some embodiments, there is provided a method for detectingand/or monitoring defects in a sealing region of a container, the methodcomprising imaging at least a part of the sealing region of thecontainer using at least one imaging camera operative at a wavelength inthe range of 0.01 μm-14 μm; wherein the imaging is performed before,during and/or after the filling of the container with a filling materialand prior to sealing of the container being completed; and determining,based on at least one frame obtained from the imaging, defects (ifpresent) in at least part of the sealing region.

According to some embodiments, the defect may be a contamination, a cut,a deformation, a bulk an uneven temperature or any combination thereof.According to some embodiments, the defect is contamination of at leastpart of the sealing region by the filling material.

According to some embodiments, the imaging is performed at an Infra-Red(IR) wavelength in the range of 8 μm-14 μm (LWIR); 3 μm-5.4 μm (MWIR); 1μm-3 μm (SWIR); 0.9 μm-1.7 μm (NIR), or any combination thereof.According to some embodiments, the imaging is performed at an Infra-Red(IR) wavelength in the range of 8 μm-14 μm (LWIR) or 3 μm-5.4 μm (MWIR);1 μm-3 μm. According to some embodiments, the imaging is performed at anInfra-Red (IR) wavelength in the range of 8 μm-14 μm (LWIR). Accordingto some embodiments, the imaging is performed after the filling of thecontainer with a filling material.

According to some embodiments, the method further comprises a secondimaging of at least part of the sealing region at a wavelength in therange of 3 μm-14 μm after the sealing of the container has beencompleted. According to some embodiments, the second imaging isperformed at a wavelength of 8 μm-14 μm. According to some embodiments,the identifying of a defect (e.g. a contamination of the sealing regionby the filling material) is further based on the second imaging of thesealing region.

According to some embodiments, the imaging is performed at a wavelengthin the range of 0.4 μm-0.76 μm, in which case the method furtherincludes a second imaging of at least part of the sealing region at awavelength in the range of 3 μm-14 μm after the sealing of the containerhas been completed. According to some embodiments, the second imaging isperformed at a wavelength of 8 μm-14 μm. According to some embodiments,the identifying of a defect (e.g. contamination of the sealing region bythe filling material) is further based on the second imaging of thesealing region. According to some embodiments, the first imaging isperformed after the filling of the container with a filling material.

According to some embodiments, the imaging further includes imaging atleast the sealing region at a wavelength in the range of 0.01 μm-0.4 μm(UV).

According to some embodiments, the container is selected from the groupconsisting of: a canister; a blister package, a tube, a heat seal bag,pouch, sachet, bottle, or any combination thereof. According to someembodiments, the container is a tube.

According to some embodiments, the filling material is selected from thegroup consisting of: a liquid, a paste, a cream, a foam, or anycombination thereof.

According to some embodiments, the method further includes heating atleast the sealing region of the container prior to the imaging thereof;thereby increasing an image contrast between the sealing region and thefilling material. According to some embodiments, heating the sealingregion comprises blowing hot air onto the sealing region. According tosome embodiments, the heating of the sealing region is performed priorto, during or after the filling of the container with the fillingmaterial.

According to some embodiments, the method further includes cooling atleast the sealing region of the container prior to the imaging thereof;thereby increasing an image contrast between the sealing region and thefilling material. According to some embodiments, the cooling of thesealing region comprises blowing cool air onto the sealing region.According to some embodiments, the cooling of the sealing region isperformed prior to, during or after the filling of the container withthe filling material.

According to some embodiments, the method further includes heating orcooling the filling material prior to the imaging; thereby increasing animage contrast between the sealing region and the filling material.According to some embodiments, the heating or cooling of the fillingmaterial is performed prior to, during or after the filling of thecontainer with the filling material.

According to some embodiments, the method further includes illuminatingthe sealing region during or after the filling of the container; therebyincreasing an image contrast between the sealing region and the fillingmaterial.

According to some embodiments, the sealing of the container is heatsealing.

According to some embodiments, there is provided a method for detecting,identifying and/or monitoring defects in a sealing region of acontainer, the method comprising imaging at least a part of the sealingregion of the container using at least one imaging camera operative at awavelength in the range of 0.01 μm-14 μm; heating and/or cooling atleast part of the container, thereby increasing an image contrastbetween the sealing region and the filling material; and determining,based on at least one frame obtained from the imaging, defects in atleast part of the sealing region.

According to some embodiments, the defect may be a contamination, a cut,a deformation, a bulk an uneven temperature or any combination thereof.According to some embodiments, the defect is contamination of at leastpart of the sealing region by the filling material.

According to some embodiments, heating/cooling the at least part of thecontainer comprises blowing hot/cold air onto and/or into the container.According to some embodiments, the imaging of the sealing region isperformed prior to, during or after the sealing of the container.According to some embodiments, the heating of the sealing region isperformed prior to, during or after the sealing of the container.

According to some embodiments, there is provided a packaging systemcomprising a package line comprising at least a filling station forfilling a container with a filling material and a sealing station forsealing of the container. The filling station comprises a first imagingcamera operative at a wavelength in the range of 0.01 μm-14 μm;positioned and configured so as to enable imaging of at least part of asealing region of the container before, during and/or after the fillingof the container with the filling material and prior to sealing of thecontainer being completed. The packaging system also includes aprocessor configured to identify defects, such as but not limited tocontamination of the sealing region by the filling material, based onimages obtained from the first camera.

According to some embodiments, the imaging by the first imaging camerais performed at an Infra-Red (IR) wavelength in the range of 8 μm-14 μm(LWIR); 3 μm-5.4 μm (MWIR); 1 μm-3 μm (SWIR); 0.9 μm-1.7 μm (NIR), orany combination thereof. According to some embodiments, the imaging bythe first imaging camera is performed at an Infra-Red (IR) wavelength inthe range of 8 μm-14 μm (LWIR) or 3 μm-5.4 μm (MWIR); 1-3 μm. Accordingto some embodiments, the imaging by the first imaging camera isperformed at an Infra-Red (IR) wavelength in the range of 8 μm-14 μm(LWIR). According to some embodiments, the imaging by the first imagingcamera is performed after the filling of the container with a fillingmaterial. According to some embodiments, the sealing station comprises asecond camera, wherein the imaging by the second camera is performed ata wavelength in the range of 3 μm-14 μm; wherein the second camera ispositioned and configured to enable imaging of at least part of thesealing region of the container after the sealing of the container hasbeen completed. According to some embodiments, the imaging by the secondimaging camera is performed at a wavelength in the range of 8 μm-14 μm.

According to some embodiments, the imaging by the first imaging camerais performed at a wavelength in the range of 0.4 μm-0.76 μm, in whichcase the sealing station further includes a second camera, positionedand configured to enable imaging of at least part of the sealing regionof the container after the sealing of the container has been completed.According to some embodiments, the imaging by the second camera isperformed at a wavelength in the range of 3 μm-14 μm. According to someembodiments, the imaging by the second imaging camera is performed at awavelength in the range of 8 μm-14 μm. According to some embodiments,the imaging by the first imaging camera is performed after the fillingof the container with a filling material.

According to some embodiments, the processor is configured to identifythe detect (e.g. contamination of the sealing region by the fillingmaterial), based on an integrated analysis of images obtained from thefirst and second cameras.

According to some embodiments, there is provided a method formonitoring, inspecting and or evaluating packaging line sealingperformance, the method comprising: obtaining a plurality of images ofat least a part of a sealing region of containers sealed on thepackaging line, obtaining and/or determining at least one packaging lineperformance parameter, applying big data analysis on the plurality ofimages and on the at least one packaging line parameter, computing atrend in the sealing performance of the packaging line based on theanalysis, and providing an indication regarding a detected and/orpredicted sealing process deficiency when the trend is indicative of adecline in the sealing performance of the packaging line.

According to some embodiments, the imagining is performed using at leastone imaging camera operative at a wavelength in the range of 0.9 μm-14μm. According to some embodiments, the camera is positioned at a sealingstation of the packaging line.

According to some embodiments, the at least one packaging lineparameters comprise one or more sealing quality parameters and/or one ormore sealing efficiency parameters.

According to some embodiments, computing the trend further comprisesdifferentiating between sealing quality and sealing efficiency.

According to some embodiments, the one or more sealing qualityparameters is selected from sealing temperature, sealing pressure,sealing time, laminate, supplier pressure applied on cutting jaws,customer complaints, flow of filling product, product weight, nozzleposition, heat of filing material, viscosity of filling material or anycombination thereof. Each possibility is a separate embodiment.

According to some embodiments, the one or more sealing efficiencyparameters is selected from time between sealer cleanups, duration ofcleanups, statistics of Teflon replacements on sealing bars, timebetween packaging line stops, duration of the stops, number of sealedproducts per minute, speed of packing line, or any combination thereof.Each possibility is a separate embodiment.

According to some embodiments, the method further comprises extractingone or more sealing features from the plurality of images. According tosome embodiments, the big data analysis is further applied on the one ormore sealing features. According to some embodiments, the sealingfeatures are selected from sealing area, sealing length, sealing width,sealing disconnection, sealing uniformity, alignment, thermal radiationor any combination thereof. Each possibility is a separate embodiment.

According to some embodiments, the method further comprises issuing analert if the computed trend is indicative of a detected or predictedsealing inefficiency. According to some embodiments, the method furthercomprises conducting a follow up inspection, wherein the follow upcomprises taking into account actions taken to overcome the detected orpredicted sealing inefficiency.

According to some embodiments, the method further (or alternatively)comprises obtaining a plurality of images captured during filling of theplurality of containers, obtaining and/or determining one or morefilling parameters, applying big data filling analysis on the pluralityof images captured during filling and on the at least one filingparameter, and computing a trend in a filing performance of thepackaging line based on the filling analysis, and providing anindication regarding a detected and/or predicted filling processdeficiency when the trend is indicative of a decline in the filingperformance of the packaging line.

According to some embodiments, the one or more filling parameters isselected from filling speed, filling amount and/or weight, presence ofcontaminants in the filling material, presence of filling material onsealing region nozzle position, heat of filing material, viscosity offilling material or any combination thereof.

According to some embodiments, the big data analysis conducted withrespect to the filling performance may be separate and/or independentfrom the big data analysis related to sealing performance. According tosome embodiments, the big data analysis conducted with respect to thefilling performance may be integrally conducted. This may for exampleenable detecting how filling performance influences sealing performance.

According to some embodiments, at times a deficient sealing may be dueto inaccuracies in the filling process rather than inaccurate orinefficient sealing.

According to some embodiments, the container is selected from the groupconsisting of: a canister; a blister package, a tube, a heat seal bag,pouch, sachet, bottle, or any combination thereof. Each possibility is aseparate embodiment.

According to some embodiments, the filling material is selected from thegroup consisting of: a liquid, a paste, a cream, a foam, or anycombination thereof. Each possibility is a separate embodiment.According to some embodiments, the filling material is a food stuff.

According to some embodiments, the sealing is heat sealing.

According to some embodiments, there is provided a packaging systemcomprising: a package line comprising at least a filling station forfilling a container with a filling material and a sealing station forsealing of the container; and an imaging camera operative at awavelength in the range of 0.01 μm-14 μm, the imaging camera positionedand configured to enable imaging of at least part of a sealing region ofcontainer processed on the packaging line; and a processor configuredto: obtain a plurality of images of at least a part of a sealing regionof the containers processed on the packaging line, wherein the imaginingis performed using at least one imaging camera operative at a wavelengthin the range of 0.9 μm-14 μm, obtain and/or determine at least onepackaging line parameter, apply big data analysis on the plurality ofimages and on the at least one packaging line parameter, compute a trendin the sealing performance of the packaging line based on the analysis,and provide an indication regarding a detected and/or predicted sealingprocess deficiency when the trend is indicative of a decline in thesealing performance. According to some embodiments, the term “decline”may refer to a decline of a predetermined size, e.g. an at least 1%, atleast 5%, or at least 10% deviation from normal. Each possibility is aseparate embodiment.

According to some embodiments, the at least one packaging lineparameters comprise one or more sealing quality parameters and/or one ormore sealing efficiency parameters.

According to some embodiments, the computing the trend further comprisesdifferentiating between sealing quality and sealing efficiency.

According to some embodiments, the one or more sealing qualityparameters is selected from sealing temperature, sealing pressure,sealing time, laminate, supplier pressure applied on cutting jaws,customer complaints, flow of filling product, product weight, nozzleposition, heat of filing material, viscosity of filling material or anycombination thereof. Each possibility is a separate embodiment.

According to some embodiments, the one or more sealing efficiencyparameters is selected from time between sealer cleanups, duration ofcleanups, statistics of Teflon replacements on sealing bars, timebetween packaging line stops, duration of the stops, number of sealedproducts per minute, speed of packing line, or any combination thereof.Each possibility is a separate embodiment.

According to some embodiments, the processor is further configured toextract one or more sealing features from the plurality of images,wherein the sealing parameters are selected from sealing area, sealinglength, sealing width, sealing disconnection, sealing uniformity,alignment, thermal radiation or any combination thereof. According tosome embodiments, the big data analysis is further applied on the one ormore sealing parameters.

Certain embodiments of the present disclosure may include some, all, ornone of the above advantages. One or more technical advantages may bereadily apparent to those skilled in the art from the figures,descriptions and claims included herein. Moreover, while specificadvantages have been enumerated above, various embodiments may includeall, some, or none of the enumerated advantages.

In addition to the exemplary aspects and embodiments described above,further aspects and embodiments will become apparent by reference to thefigures and by study of the following detailed descriptions.

BRIEF DESCRIPTION OF THE FIGURES

Examples illustrative of embodiments are described below with referenceto figures attached hereto. In the figures, identical structures,elements, or parts that appear in more than one figure are generallylabeled with a same numeral in all the figures in which they appear.Alternatively, elements or parts that appear in more than one figure maybe labeled with different numerals in the different figures in whichthey appear. Dimensions of components and features shown in the figuresare generally chosen for convenience and clarity of presentation and arenot necessarily shown in scale. The figures are listed below.

FIG. 1 schematically illustrates a process and system for monitoringsealing efficiency of a tube by applying thermographic imaging; whereinthe thermographic camera is positioned at the filing station of thepackaging process; according to some embodiments;

FIG. 2 schematically illustrates a process for monitoring sealingefficiency of a tube during using thermographic imaging; wherein thethermographic camera is positioned at the hot air heating station of thepackaging process; according to some embodiments;

FIG. 3 schematically illustrates a process for monitoring sealingefficiency of a tube during using thermographic imaging; wherein thethermographic camera is positioned at the sealing station of thepackaging process; according to some embodiments;

FIG. 4 schematically illustrates a process for monitoring sealingefficiency of a tube during using thermographic imaging; wherein thethermographic camera is positioned after the sealing station of thepackaging process; according to some embodiments;

FIG. 5 schematically illustrates a process for monitoring sealingefficiency of a tube during using thermographic imaging; wherein a firstthermographic camera is positioned at the filling station and a secondthermographic camera is positioned after the sealing station of thepackaging process; according to some embodiments; and

FIG. 6 schematically illustrates a process for monitoring sealingefficiency of a tube during using a combination of visual and thermalimaging; wherein a first visual camera is positioned at the fillingstation and a second thermographic camera is positioned after thesealing station of the packaging process; according to some embodiments

FIG. 7A shows the hereindisclosed system for thermographic imaging(thermal imaging) of containers, as implemented for lunch meatpackaging, before sealing of the container;

FIG. 7B shows the hereindisclosed system for thermographic imaging(thermal imaging) of containers, as implemented for lunch meatpackaging, after sealing of the container;

FIG. 8A shows a representative thermal image (upper panel) obtainedprior to sealing of a container (black pixels representing cold objectsand white pixels representing hot objects) and a graph (lower panel)showing the change in pixel intensity in the image;

FIG. 8B shows a representative visual image (upper panel) obtained priorto sealing of a container (red channel) and a graph (lower panel)showing the change in pixel intensity in the image.

FIG. 9 is an illustrative flowchart of a method for monitoring and/orinspecting a packaging line's sealing performance, according to someembodiments.

FIG. 10 is an illustrative flowchart of a method for monitoring and/orinspecting packaging line's filling performance, according to someembodiments.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, various aspects of the disclosure will bedescribed. For the purpose of explanation, specific configurations anddetails are set forth in order to provide a thorough understanding ofthe different aspects of the disclosure. However, it will also beapparent to one skilled in the art that the disclosure may be practicedwithout specific details being presented herein. Furthermore, well-knownfeatures may be omitted or simplified in order not to obscure thedisclosure.

According to some embodiments, there is provided a method for detecting,identifying and/or monitoring defects in a sealing region of acontainer, the method comprising imaging at least a sealing region ofthe container during and/or after the filling of the container with afilling material and prior to the completion of sealing of the containerusing a camera (also referred to herein as “pre-sealing imaging”), anddetermining, identifying and/or monitoring the defect (e.g.contamination of the sealing region by the filling material) based onthe imaging. According to some embodiments, the camera is operative at awavelength in the range of 0.01 μm-14 μm. According to some embodiments,the camera may be any camera enabling thermal imaging, such as, but notlimited to, Mid Wave Infra-Red (MWIR), operative at a wavelength in therange of 3 μm-5.4 μm or Long Wave Infra-Red (LWIR), operative at awavelength in the range of 8 μm-14 μm.

According to some embodiments, the defects identified may includecontamination, a cut, a deformation, a bulk an uneven temperature or anycombination thereof. Each possibility is a separate embodiment.According to some embodiments, the defect is a contamination of at leastpart of the sealing region by the filling material.

According to some embodiments, the method enablesanticipating/estimating sealing efficiency prior to the sealing of thecontainer. According to some embodiments, the method is for anticipatingand/or estimating sealing efficiency of a heat-sealed container prior toits sealing being completed.

According to some embodiments, the method may be applicable fordetermining welding efficiency of an object formed of welded parts.Non-limiting examples of objects formed of welded parts includeheat-formed packages (e.g. plastic blisters), tubes including two ormore tube elements welded together (e.g. breath sample tubes includingfilters and the like). According to some embodiments, the welding mayinclude hot gas welding, laser welding and/or ultrasonic welding. Eachpossibility is a separate embodiment. According to some embodiments, thewelding may include applying or melting adhesives or by melting thepackaging material together using conduction, induction heating orultrasonic welding methods. Each possibility is a separate embodiment.According to some embodiments, determining welding efficiency of anobject includes determining at least one parameter related to thequality of the welding.

As used herein the terms “container” and “package” may be usedinterchangeably and refer to any packaging meant suitable for containinga filling material and sized and shaped to enable filling and sealing ona package line. According to some embodiments, the container may be aprimary container, i.e. the package that first envelops the product andholds it. Non-limiting examples of suitable containers include canisters(such as, but not limited to, yogurt canisters, canisters containingcosmetic products, and the like), blister packages (such as, but notlimited to, blisters used for packaging of medical equipment,medicaments, batteries, and more), tubes (such as, but not limited to,toothpaste tubes or cosmetic tubes), heat seal bags or sachets (such as,but not limited to, heat sealed bags used for food packing, for packingof medical equipment, and the like), or any combination thereof. Eachpossibility is a separate embodiment.

As used herein, the term “sealing region” refers to part of thecontainer which, after filling of the package, is configured to ensureits sealing. According to some embodiments, sealing of the package isconsidered to be completed once opposite sides of the sealing regionhave been pressed together, after or while applying heat thereto.According to some embodiments, the sealing may be heat sealing.

As used herein, the term “contamination” refers to spills/splashes offilling material or other material on at least the sealing region orpart of the package; which impair the sealing of the package. Accordingto some embodiments, the contamination may refer to uncleanness of thesealing region caused during the filling of the container with thefilling material.

As used herein” the term “filling material” refers to the product filledin/contained within the container. According to some embodiments, thefilling material may be a liquid, a paste, a cream, a foam, or anycombination thereof. Each possibility is a separate embodiment.According to some embodiments, the filling material may be colorless,transparent, white, cream-colored, light-pink colored, or having a colorsimilar to the color of at least the sealing region of the container.Each possibility is a separate embodiment.

According to some embodiments, the filling material may be food stuff,such as but not limited to lunch meats, cheeses, spreads, yogurts, andthe like. Each possibility is a separate embodiment

According to some embodiments, determining and/or identifying defectsthe sealing region of the container includes processing of imagesobtained during the filling. According to some embodiments, the imagingmay include obtaining at least two images of the container during and/orafter the filing of the container. As used herein, the term “at leasttwo”, when referring to the images obtained during imaging may refer to2, 3, 4, 5 or more images. Each possibility is a separate embodiment.

According to some embodiments, obtaining two or more images, e.g. ininterval may enable differentiating between signals caused by radiationfrom the sealing region and reflection caused by the packaging materialitself. This due to the fact that the radiation gradually decreases asthe sealing region cools down whereas reflection stays intact. That is,according to some embodiments, the determining of at least one parameterrelated to the quality of the object may be based on an integratedanalysis of the images taking into account the cooling of the sealingregion over time and the interval between the image frames taken.

According to some embodiments, image processing may include applyingimage processing algorithms. According to some embodiments, the imageprocessing may include image contrast analysis, edge detection, imagearithmetic, cross correlation between images, convolution between imagesor between an image to a predefined kernel, spatial frequencytransformation and/or spatial filtering methods, temporal frequencytransformation and temporal filtering methods, Fourier transforms,discrete Fourier transforms, discrete cosine transforms, morphologicalimage processing, finding peaks and valleys (low and high intensityareas), image contours recognition, boundary tracing, line detection,texture analysis, histogram equalization, image deblurring, clusteranalysis or any other suitable image processing known in the art orcombinations thereof. Each possibility is a separate embodiment.According to some embodiments, the image processing may include deeplearning. According to some embodiments, the image processing mayinclude open CV. According to some embodiments, thedetermination/identification of a defect container (e.g. a containerwith a contaminated sealing region) may result in ejection of thecontainer from the packaging line, arrest of the packaging process orany other suitable action required to prevent an improperly sealedcontainer to discharged for distribution.

According to some embodiments, the system/method may further beconfigured to identify trends indicative of and/or responsible for aninefficient filling of the container, such as, but not limited to,inaccurate nozzle position, speed of packing line movement, heat offiling material, viscosity, and the like. According to some embodiments,the identifying of trends may include big-data analysis and/or machinelearning techniques. According to some embodiments, when a defectivetrend is identified, the packaging line may be halted for inspection,calibration, and/or the like, thereby preventing defective sealing inmultiple containers.

According to some embodiments, the system/method may be configured toidentify packaging/sealing parameters, prior to the filling of a targetcontainer. As a non-limiting example, the system/method may beconfigured to identify/detect defects of the container's sealing region,prior to the filling thereof. As another non-limiting example, thesystem/method may be further configured to identify an improperorientation of a container prior to its filling. According to someembodiments, the correct/improper orientation of the container may bedefined based on text printed on the container and/or the orientation ofthe sealing region and/or the shape of the container (e.g. of asymmetriccontainers).

According to some embodiments, the system/method may be configured toidentify packaging/sealing parameters, after the filling but beforeheating of the target container's sealing region. As a non-limitingexample, the system/method may be configured to identify/detect defects(e.g. contamination of the container's sealing region) after the fillingbut prior to the heating thereof. As another non-limiting example, thesystem/method may be further configured to identify an improperorientation of a container after filling but before the heating thereof.As another non-limiting example, the system/method may be configured todetermine if a correct amount/quantity of filling material has enteredthe container after filling but before the heating thereof.

According to some embodiments, the system/method may be configured toidentify packaging/sealing parameters, after the heating but beforesealing of the target container's sealing region. As a non-limitingexample, the system/method may be configured to identify/detect defects(e.g. contamination of the container's sealing region) after the heatingbut prior to the sealing thereof. As another non-limiting example, thesystem/method may be configured to identify an improper orientation of acontainer after heating but before the sealing thereof. As anothernon-limiting example, the system/method may be configured to determineif a correct amount/quantity of filling material has entered thecontainer after heating but before the sealing thereof. As anothernon-limiting example, the system/method may be configured to determineif a correct/sufficient heating level of the sealing region has beenreached prior to the sealing of the container. As another non-limitingexample, the system/method may be configured to determine if correctwidth, location, and alignment of the sealing region.

According to some embodiments, the system/method may be configured toidentify packaging/sealing parameters, after the sealing of the targetcontainer. As a non-limiting example, the system/method may beconfigured to determine whether as sufficient level of heating has beenreached during sealing. As another non-limiting example, thesystem/method may be configured to determine the alignment and width ofthe sealing line. As another non-limiting example, the system/method maybe configured to detect unsealed and/or improperly sealed parts/regionsof the sealing line. As a non-limiting example, the system/method may beconfigured to determine whether a sufficient level of pressure has beenreached during sealing. As a non-limiting example, the system/method maybe configured to determine whether as sufficient time duration has beenreached during sealing

According to some embodiments, the imaging may be infra-red (IR)imaging. According to some embodiments, the imaging may be thermalimaging. According to some embodiments, the imaging may be MWIR imaging.According to some embodiments, the imaging may be LWIR imaging.According to some embodiments, the imaging is performed at a wavelengthin the range of 0.76 μm-14 μm. According to some embodiments, the IRimaging may be short wave-imaging, medium wave imaging, long waveimaging or combinations thereof. Each possibility is a separateembodiment. As a non-limiting example, the imaging may include obtainingimages (one or more) in the short-wave spectrum, images (one or more) inthe medium wave spectrum and/or images (one or more) in the long wavespectrum (one or more) of the same container. According to someembodiments, the IR imaging may be performed at a wavelength in therange of 8 μm-14 μm; 3 μm-5.4 μm; 1-3 μm; 0.9 μm-1.7 μm, or anycombination thereof. Each possibility is a separate embodiment. As anon-limiting example, the imaging of a container may include obtainingframes in each of or some of the aforementioned wavelength ranges.

According to some embodiments, the imaging may be UV imaging. Accordingto some embodiments, the imaging may be performed at a wavelength in therange of 0.01 μm-0.4 μm. According to some embodiments, the UV imagingmay be done instead of or in combination with the IR imaging.

According to some embodiments, the imaging may be visible light imaging.According to some embodiments, the imaging may be performed at awavelength in the range of 0.4 μm-0.76 μm. According to someembodiments, the visible light imaging may be done in combination withthe IR imaging and/or UV imaging. For example, the visible imaging maybe used in combination with IR imaging enabling detection of defectsbased on both thermal changes and changes in color.

According to some embodiments, the method may include heating at leastthe sealing region of the container prior to the imaging thereof;thereby increasing an image contrast between the sealing region and thefilling material. According to some embodiments, heating the containercomprises heating the container to a temperature above 30° C., above 35°C., above 40° C., or above 50° C. Each possibility is a separateembodiment. According to some embodiments, the heating of the sealingregion comprises blowing hot air onto the sealing region, into thecontainer and/or on the outside of the container. According to someembodiments, the heating of the sealing region may be done prior to,during, or after the filling of the container with the filling material.Each possibility is a separate embodiment.

According to some embodiments, the method may include cooling at leastthe sealing region of the package prior to the imaging thereof; therebyincreasing an image contrast between the sealing region and the fillingmaterial. According to some embodiments, cooling the container comprisescooling the container to a temperature below 20° C., below 15° C., below10° C., or below 5° C. Each possibility is a separate embodiment.According to some embodiments, the cooling of the sealing regioncomprises blowing cool air onto the sealing region, into the containerand/or on the outside of the container. According to some embodiments,the cooling of the sealing region may be done prior to, during or afterthe filling of the container with the filling material. Each possibilityis a separate embodiment.

According to some embodiments, the method may include heating or coolingthe filling material prior to the imaging; thereby increasing an imagecontrast between the sealing region and the filling material. Accordingto some embodiments, heating the filling material comprises heating thefilling material to a temperature above 30° C., above 35° C., above 40°C., or above 50° C. Each possibility is a separate embodiment. Accordingto some embodiments, cooling the filling material comprises cooling thefilling material to a temperature below 20° C., below 15° C., below 10°C., or below 5° C. Each possibility is a separate embodiment. Accordingto some embodiments, the heating or cooling of the filling material maybe done prior to, during or after the filling of the container with thefilling material. Each possibility is a separate embodiment.

According to some embodiments, the method may include illuminating atleast the sealing region of the container before, during or after thefilling of the container; thereby increasing an image contrast betweenthe sealing region and the filling material. According to someembodiments, the illumination may be IR illumination, visible lightillumination, UV illumination, micro wave radiation, or combinationsthereof. Each possibility is a separate embodiment.

According to some embodiments, the method may further include imaging atleast the sealing region of the container after the sealing of thecontainer (post-sealing imaging), using a second camera. According tosome embodiments, the second camera may be operative at a wavelength inthe range of 0.01 μm-14 μm. According to some embodiments, theafter-sealing imaging may be infra-red (IR) imaging, as essentiallydescribed for the pre-sealing imaging. According to some embodiments,the after-sealing imaging may be UV imaging, as essentially describedfor the pre-sealing imaging. According to some embodiments, theafter-sealing imaging may be visible light imaging, as essentiallydescribed for the pre-sealing imaging.

According to some embodiments, the pre-sealing and post-sealing imagingmay be the same or different. As a non-limiting example, the pre-sealingimaging may be done using visual imaging, whereas the post-sealingimaging is done using IR thermal imaging. As another non-limitingexample, the pre-sealing imaging may be done using short wave IRimaging, whereas the post-sealing imaging is done using long wave IRthermal imaging, including MWIR and LWIR IR thermal imaging. As anothernon-limiting example, the pre-sealing imaging may be done using acombination of IR imaging and visible imaging, whereas the post-sealingimaging is done using IR imaging alone.

According to some embodiments, the imaging may be visible light imaging.According to some embodiments, the imaging may be performed at awavelength in the range of 0.4 μm-0.76 μm. According to someembodiments, the visible light imaging may be done in combination withthe IR imaging and/or UV imaging.

According to some embodiments, the method further includes evaluatingsealing integrity, based on an integrated analysis of pre-sealing andpost-sealing imaging. As used herein, the term “integrated analysis” mayrefer to image processing including applying processing algorithms topre-sealing and post-sealing images and identifying improper sealingbased on image parameters deduced from at least one pre-sealing imageand at least one post-sealing image.

According to some embodiments, the method further includes squeezing orotherwise applying pressure on the container, prior to the post-sealingimaging. Squeezing the container will, in the case of incompletesealing, result in small amounts of filler material to leak out of thecontainer. Advantageously, thermal imaging of the container allowsdetecting such leaks, and thus improper sealing of the container due toits high sensitivity to differences in the emissivity of a product andits low sensitivity to reflections.

According to some embodiments, there is provided a packaging systemcomprising a packaging line comprising at least a filling station forfilling a container with a filling material and a sealing station forsealing of the container. The filing station of the package lineincludes a camera operative at a wavelength in the range of 0.01 μm-14μm. The camera is positioned and configured to enable imaging of atleast a part of the sealing region of the container during and/or afterthe filling of the container with the filling material and prior to thesealing of the container being completed. According to some embodiments,the packaging line further includes a processor configured to identifydefects (e.g. contamination of the sealing region by the fillingmaterial), based on images obtained from the camera.

As used herein, the terms “packaging line” and “package line” may beused interchangeably and refer to an automatic process of enclosingproducts within containers or any kind of packages. According to someembodiments, the term refers to automated enclosing of products withinheat-sealed containers. According to some embodiments, the package lineincludes at least a filling station and a sealing station. As usedherein the term “filling station” refers to part of the packaging linewhere the filling material is poured into, sprayed into, or otherwisedispensed into the container. As used herein, the term “sealing station”refers to part of the packaging line where the container holding thefilling material is hermetically sealed by pressing together the shoresof the container's sealing region after or during a heating phase.According to some embodiments, the packaging line further includes aheating station, located prior to the sealing station. As used herein,the term “heating station” refers to part of the packaging line wherethe sealing region is heated in preparation for sealing. According tosome embodiments, the sealing further includes applying an adhesive tothe shores prior to the shores being pressed together. According to someembodiments, the packaging line further includes a discharge station. Asused herein the term “discharge station” refers to part of the packagingline where the sealed container is forwarded for further processing,such as, but not limited to, secondary packaging.

As used herein, the term “big data analysis” may refer to a form ofadvanced analytics, which involve complex applications with elementssuch as predictive models, statistical algorithms and what-if analysispowered by analytics systems. According to some embodiments, the bigdata analysis may have a holistic approach that relies on:

-   -   Trend analysis, for failure prevention.    -   SPC (Statistical Process Control) software, for root cause        analysis.

According to some embodiments, the big data analysis is advantageouslyprovides the following benefits:

-   -   Improvement of production efficiency/yield    -   Increased Mean Time Between Failures (MTBF)—by alerting on a        trend indicating reduced quality/efficiency of the sealing        and/or filling of containers, a failure that has not yet        happened can be treated on time e.g. during routine maintenance.    -   Reduced failure time (i.e. the time during which the packaging        line is in a failure mode)    -   Increasing production rate inter alia due to the confidence in        the real time quality analysis of the products.

According to some embodiments, the big data analysis may includeapplying machine learning algorithms on the plurality of images and theat least one packaging line parameter. According to some embodiments,the machine learning may be supervised. According to some embodiments,the machine learning may be unsupervised. According to some embodiments,the machine learning may include feature extraction. According to someembodiments, the machine learning may include neural network algorithms.

According to some embodiments, the camera is positioned at the fillingstation of the process line. According to some embodiments, the camerais positioned at the heating station of the process line. According tosome embodiments, the camera is positioned at the sealing station of theprocess line.

According to some embodiments, the camera is an IR camera (e.g. NIR,SWIR) and/or IR thermographic (thermal imaging) camera (e.g. MWIR, LWIR)configured to enable IR imaging as essentially described herein.Additionally or alternatively, the camera may enable imaging at thevisible and/or UV spectrum, as essentially described herein.

According to some embodiments, the processor unit may be an integralpart of the packaging line. According to some embodiments, the processormay be an external and/or adjunct to the computing device, such as, butnot limited to, a mobile, smartphone, tablet, pc, or any dedicatedcomputing device. Each possibility is a separate embodiment. Accordingto some embodiments, the processor may be a virtual processor, such asan internet enabled device (i.e. cloud computing). According to someembodiments, the processor may be configured to identify defects in thesealing region by performing image processing, e.g. applying imageprocessing algorithms, on the images obtained from the camera, asessentially described herein.

According to some embodiments, the sealing station comprises a secondcamera (online or offline) operative at a wavelength in the range of0.01 μm-14 μm; wherein the second camera is positioned and configured toenable imaging of at least the sealing region of the container after thesealing of the container has been completed. According to someembodiments, the second camera may be positioned at the dischargestation of the package line. According to some embodiments, the firstand/or second cameras are thermographic cameras. According to someembodiments, the first and/or second cameras are configured to operateat a wavelength in the range of 0.76 μm-14 μm. According to someembodiments, the first and/or second imaging is performed at awavelength in the range of 8 μm-14 μm; 3 μm-5.4 μm; 1 μm-3 μm; 0.9μm-1.7 μm, or any combination thereof. Each possibility is a separateembodiment. For example, the imaging may include obtaining frames ineach of or some of the aforementioned wavelength ranges, as essentiallydescribed herein.

According to some embodiments, the packaging line may include more thantwo cameras, such as 3, 4, 5 or more cameras. These cameras may bedistributed along the stations of the packaging line (e.g. one at eachstation). According to some embodiments, a particular station mayinclude more than one camera; while other stations may include one onlyor be devoid of cameras.

According to some embodiments, the processor may be further configuredto identify improper sealing, based on an integrated analysis of imagesobtained from the first and second cameras. According to someembodiments, the integrated analysis may include applying processingalgorithms to images obtained from both cameras and identifying impropersealing based on image parameters deduced/extrapolated from at least oneimage obtained from each of the cameras.

According to some embodiments, the packaging line further comprises a“quality control (QC) station” at which the container is squeezed orotherwise has pressure applied thereon. According to some embodiments,the QC station is positioned after the sealing station, but prior to thesecond camera.

Reference is now made to FIG. 1, which schematically illustrates asystem 100 for monitoring sealing efficiency of a tube 110, usingthermographic imaging (thermal imaging). System 100 includes apre-sealing phase, indicated as phase A and a post-sealing phaseindicated as phase B. Phase A includes a filling station 120, a heatingstation 130, and a sealing station 140. Phase B includes a dischargestation 150, where tube 110, now hermetically sealed, can be forwardedfor further processing. System 100 includes a thermal imaging IR camera125 positioned at filling station 120 and configured to image a sealingregion 112 of tube 110 during filling of tube 110 with filling material122. The imaging by thermal imaging IR camera 125 may be performed in awavelength in the range of 3 μm-14 μm, preferably 8 μm-14 μm. System 100further includes a processor (not shown) configured to obtain one ormore image frames from thermal imaging IR camera 125, to process theimages and to identify defects, such as contamination 124 on sealingregion 112 of tube 110. According to some embodiments, once a defect isidentified, tube 110 may be ejected from the processing line, prior toreaching the next station, here prior to reaching heat station 130.Alternatively, defect tube 110 may be ejected at discharge station 150.According to some embodiments, the processor may be further configuredto identify trends and/or operational mistuning in the filling station,based on images of one or more tubes passing through the packaging line,as essentially described herein.

Reference is now made to FIG. 2, which schematically illustrates asystem 200 for monitoring sealing efficiency of a tube 210, usingthermographic imaging. System 200 includes a pre-sealing phase,indicated as phase A and a post-sealing phase indicated as phase B.Phase A includes a filling station 220, a heating station 230, and asealing station 240. Phase B includes a discharge station 250, wheretube 210, now hermetically sealed, can be forwarded for furtherprocessing. System 200 includes a thermal imaging IR camera 235positioned at heating station 230 and configured to image a sealingregion 212 of tube 210 during heating of sealing region 212 by hot air232. The imaging by thermal imaging IR camera 235 may be performed in awavelength in the range of 3 μm-14 μm, preferably 8 μm-14 μm. System 200further includes a processor (not shown) configured to obtain one ormore image frames from thermal imaging IR camera 235, to process theimages and to identify defects, such as contamination 224 on sealingregion 212 of tube 210. According to some embodiments, once a defect isidentified, tube 210 may be ejected from the processing line, prior toreaching the next station, here prior to reaching sealing station 240.Alternatively, defect tube 210 may be ejected at discharge station 250.According to some embodiments, the processor may be further configuredto identify trends and/or operational mistuning in the hot air heatingstation, based on images of one or more tubes passing through thepackaging line, as essentially described herein.

Reference is now made to FIG. 3, which schematically illustrates asystem 300 for monitoring sealing efficiency of a tube 310, usingthermographic imaging. System 300 includes a pre-sealing phase,indicated as phase A and a post-sealing phase indicated as phase B.Phase A includes a filling station 320, a heating station 330, and asealing station 340. Phase B includes a discharge station 350, wheretube 310, now hermetically sealed, can be forwarded for furtherprocessing. System 300 includes a thermal imaging IR camera 345positioned at sealing station 330 and configured to image a sealingregion 312 of tube 310 during sealing of sealing region 312 by pressingplates 342. The imaging by thermal imaging IR camera 345 may beperformed in a wavelength in the range of 3 μm-14 μm, preferably 8 μm-14μm. System 300 further includes a processor (not shown) configured toobtain one or more image frames from thermal imaging IR camera 345, toprocess the images and to identify defects, such as contamination 324 onsealing region 312 of tube 310. According to some embodiments, once adefect is identified, tube 310 may be ejected from the processing line,prior to reaching the next station, here prior to reaching dischargestation 350. Alternatively, defect tube 310 may be ejected at dischargestation 350. According to some embodiments, the processor may be furtherconfigured to identify trends and/or operational mistuning in thesealing station, based on images of one or more tubes passing throughthe packaging line, as essentially described herein.

Reference is now made to FIG. 4, which schematically illustrates asystem 400 for monitoring sealing efficiency of a tube 410, usingthermographic imaging. System 400 includes a pre-sealing phase,indicated as phase A and a post-sealing phase indicated as phase B.Phase A includes a filling station 420, a heating station 430, and asealing station 440. Phase B includes a discharge station 450, wheretube 410, now hermetically sealed, can be forwarded for furtherprocessing. System 400 includes a thermal imaging IR camera 455positioned at discharge station 450 and configured to image a sealingregion 412 of tube 410 after sealing of sealing region 412 by pressingplates 442. The imaging by thermal imaging IR camera 455 may beperformed in a wavelength in the range of 3 μm-14 μm, preferably 8 μm-14μm. System 400 further includes a processor (not shown) configured toobtain one or more image frames from thermal imaging IR camera 455, toprocess the images and to identify defects, such as contamination 424 onsealing region 412 of tube 410. According to some embodiments, defecttube 410 may be ejected at discharge station 450.

Reference is now made to FIG. 5, which schematically illustrates asystem 500 for monitoring sealing efficiency of a tube 510, usingthermographic imaging. System 500 includes a pre-sealing phase,indicated as phase A and a post-sealing phase indicated as phase B.Phase A includes a filling station 520, a heating station 530, and asealing station 540. Phase B includes a discharge station 550, wheretube 510, now hermetically sealed, can be forwarded for furtherprocessing. System 500 includes a first thermal imaging IR camera 525positioned at filling station 520 and configured to image a sealingregion 512 of tube 510 during filling of tube 510 with filling material522. System 500 further includes a second thermal imaging IR camera 555positioned at discharge station 550 and configured to image a sealingregion 512 of tube 510 after sealing of sealing region 512 by pressingplates 542. The imaging by first thermal imaging IR camera 535 andsecond thermal imaging IR camera 555 may be performed in a wavelength inthe range of 3 μm-14 μm, preferably 8 μm-14 μm. System 500 furtherincludes a processor (not shown) configured to obtain one or more imageframes from first thermal imaging IR camera 535 and from second thermalimaging IR camera 555 and to process the images obtained to identifydefects, such as contamination 524 on sealing region 512 of tube 510 andto evaluate sealing integrity. According to some embodiments, defecttube 510 may be ejected at discharge station 550. First thermal imagingIR camera 525 and second thermal imaging IR camera 555 are here shown tobe located at the filling station and at the discharge station,respectively. It is however understood that other positions are likewiseapplicable and within the scope of this disclosure. It is furtherunderstood, that system 500 may include more than two cameras, asessentially described herein.

Reference is now made to FIG. 6, which schematically illustrates asystem 600 for monitoring sealing efficiency of a tube 610, usingimaging. System 600 includes a pre-sealing phase, indicated as phase Aand a post-sealing phase indicated as phase B. Phase A includes afilling station 620, a heating station 630, and a sealing station 640.Phase B includes a discharge station 650, where tube 610, nowhermetically sealed, can be forwarded for further processing. System 600includes a first visual imaging camera 625 positioned at filling station620 and configured to image a sealing region 612 of tube 610 duringfilling of tube 610 with filling material 622. The imaging by firstvisual imaging camera 625 is performed in a wavelength in the visiblespectrum range of 0.4 μm-0.76 μm and may be configured to identifydefects based on visual changes (e.g. color stains) on tube 610. System600 further includes a second thermal imaging IR camera 655 positionedat discharge station 650 and configured to image a sealing region 612 oftube 610 after sealing of sealing region 612 by pressing plates 642. Theimaging by second thermal imaging IR camera 655 is performed in awavelength in the range of 3 μm-14 μm, preferably 8 μm-14 μm. System 600further includes a processor (not shown) configured to obtain one ormore image frames from first visual imaging camera 635 and from secondthermal imaging IR camera 655, and to process the images to identifydefects, such as contamination 624 on sealing region 612 of tube 610 andto evaluate sealing integrity. It is understood that neither imaging inthe visual spectrum alone nor thermal imaging after completion ofsealing alone are insufficient for reliable evaluation of the integrityof tube 610 whereas the combined pre-sealing visual imaging andpost-sealing thermal imaging may provide a reliable measure. Accordingto some embodiments, defect tube 610 may be ejected at discharge station650. First visual imaging camera 635 and second thermal imaging IRcamera 655 are here shown to be located at the filling station and atthe discharge station, respectively. It is however understood that otherpositions are likewise applicable and within the scope of thisdisclosure. It is further understood, that system 600 may include morethan two cameras, as essentially described herein.

With reference to the above Figures, it is understood that the cameramay be positioned so as to directly focus on the container, as depicted,or indirectly using mirrors, beam splitters, lenses or any other opticalelement allowing indirect imaging of the container.

-   -   Reference may now be made to FIG. 9, is an illustrative        flowchart of a method for improving packaging line sealing        performance 900, according to some embodiments. According to        some embodiments, the packaging may be of any filling material        such as a liquid, a paste, a cream, a foam, a food stuff (e.g. a        snack) in any kind of container such as a blister package, a        tube, a heat seal bag, pouch, sachet, bottle. Each possibility        is a separate embodiment.

In step 910 of the method a plurality of images of at least a part of asealing region of containers sealed (e.g. by heat-sealing) on thepackaging line is obtained. According to some embodiments, the imaginingis performed using at least one imaging camera operative at a wavelengthin the range of 0.9 μm-14 μm. According to some embodiments, the camerais positioned at a sealing station of the packaging line. According tosome embodiments, one or more sealing features may be extracted from theplurality of images. According to some embodiments, the featureextraction may be accomplished using feature extraction/selectiontechniques. According to some embodiments, the sealing features areselected from sealing area, sealing length, sealing width, sealingdisconnection, sealing uniformity, alignment, thermal radiation or anycombination thereof. Each possibility is a separate embodiment.According to some embodiments, the feature extraction may be automatede.g. be accomplished by applying deep learning algorithms on theplurality of images.

In step 920, at least one packaging line performance parameter isobtained (e.g. from a sensor) and/or determined (e.g. input by a user asa result of inspection of the packaging line). According to someembodiments, the at least one packaging line parameters comprise one ormore sealing quality parameters and/or one or more sealing efficiencyparameters. According to some embodiments, the one or more sealingquality parameters is selected from sealing temperature, sealingpressure, sealing time, laminate, supplier pressure applied on cuttingjaws, customer complaints, flow of filling product, product weight,nozzle position, heat of filing material, viscosity of filling materialor any combination thereof. Each possibility is a separate embodiment.According to some embodiments, the one or more sealing efficiencyparameters is selected from time between sealer cleanups, duration ofcleanups, statistics of Teflon replacements on sealing bars, timebetween packaging line stops, duration of the stops, number of sealedproducts per minute, speed of packing line, or any combination thereof.Each possibility is a separate embodiment. According to someembodiments, one or more features may be extracted from the at least onepackaging line performance parameter for example by using featureextraction/selection techniques. According to some embodiments, one ormore features may be extracted from the at least one packaging lineperformance parameter by applying neural network algorithms.

In step 930, big data analysis is applied on the plurality of images andon the at least one packaging line parameter (and/or parametersextracted therefrom). According to some embodiments, the big dataanalysis may include applying machine learning algorithms on theplurality of images and the at least one packaging line parameter.According to some embodiments, the machine learning may be supervised.According to some embodiments, the machine learning may be unsupervised.

In step 940, a trend in the sealing performance of the packaging linemay be computed. Advantageously, the trend may enable detecting that adefect in sealing of the containers is about to happen, rather than onlydetecting defects as they happen. According to some embodiments, the bigdata analysis may further enable differentiating between declines insealing quality (i.e. reduced sealing integrity) and declines in sealingefficiency (e.g. increased duration of the sealing process). Accordingto some embodiments, the big data analysis may further detect theunderlining cause of a decline in the sealing performance of thepackaging line, based on the big data analysis (e.g. the underlyingcause of sealing integrity and/or efficiency).

In step 950, an indication regarding a detected and/or predicted sealingprocess deficiency may optionally be issued, when the trend isindicative of a decline in the sealing performance of the packagingline. According to some embodiments, the method further comprisesissuing an alert if the computed trend is indicative of a detected orpredicted sealing inefficiency. According to some embodiments, themethod further comprises conducting a follow up inspection, wherein thefollow up comprises taking into account actions taken to overcome thedetected or predicted sealing inefficiency.

Reference may now be made to FIG. 10, is an illustrative flowchart of amethod for monitoring, inspecting and/or evaluating packaging linefiling performance 1000, according to some embodiments. According tosome embodiments, the filling material may be of any filling materialsuch as a liquid, a paste, a cream, a foam, a food stuff (e.g. a snack)in any kind of container such as a blister package, a tube, a heat sealbag, pouch, sachet, bottle. Each possibility is a separate embodiment.According to some embodiments, the evaluation of filling performance maybe integrally part of the method 900. Alternatively, method 1000 may bea stand-alone method.

In step 1010, a plurality of images captured during filling of aplurality of containers is obtained. According to some embodiments, theimagining is performed using at least one imaging camera operative at awavelength in the range of 0.9 μm-14 μm. According to some embodiments,the camera is positioned at a filing station of the packaging line.According to some embodiments, one or more filing features may beextracted from the plurality of images. According to some embodiments,the feature extraction may be accomplished using featureextraction/selection techniques. According to some embodiments, thefeature extraction may be automated e.g. be accomplished by applyingdeep learning algorithms on the plurality of images.

In step 1020, at least one filing parameter is obtained (e.g. from asensor) and/or determined (e.g. input by a user as a result ofinspection of the filling station). According to some embodiments, theat least one filling parameters comprise one or more of filling speed,filling amount and/or weight, presence of contaminants in the fillingmaterial, presence of filling material on sealing region nozzleposition, heat of filing material, viscosity of filling material or anycombination thereof. Each possibility is a separate embodiment.

In step 1030, big data analysis is applied on the plurality of imagesand on the at least one filling parameter (and/or parameters extractedtherefrom). According to some embodiments, the big data analysis mayinclude applying machine learning algorithms on the plurality of imagesand the at least one filling parameter. According to some embodiments,the machine learning may be supervised. According to some embodiments,the machine learning may be unsupervised.

In step 1040, a trend in the filling performance of the packaging linemay be computed. Advantageously, the trend may enable detecting that adefect in filling of the containers is about to happen, rather than onlydetecting defects as they happen. According to some embodiments, the bigdata analysis may further enable differentiating between declines infilling quality (e.g., imprecise filling and/or contamination of thesealing etc.) and declines in sealing efficiency (e.g. increasedduration of the filling process). According to some embodiments, the bigdata analysis may further detect the underlining cause of a decline inthe filling performance of the packaging line, based on the big dataanalysis (e.g. the underlying cause of filling integrity and/orefficiency), such as bit not limited to inaccurate nozzle position.

In step 1050, an indication regarding a detected and/or predictedfilling deficiency may optionally be issued, when the trend isindicative of a decline in the filling performance of the packagingline. According to some embodiments, the method further comprisesissuing an alert if the computed trend is indicative of a detected orpredicted filling inefficiency. According to some embodiments, themethod further comprises conducting a follow up inspection, wherein thefollow up comprises taking into account actions taken to overcome thedetected or predicted filling inefficiency.

EXAMPLES Example 1—Thermal Imaging of Lunch Meat Packaging

Lunch meat packaging was evaluated using the hereindisclosed system forpre-sealing thermographic imaging (thermal imaging) using a thermalimaging IR camera. The setup of the system before and after sealing ofthe container is shown in FIG. 7A and FIG. 7B, respectfully. camera 710was positioned at the filling station so as to image container 720before (as shown in FIG. 7A) and during sealing of container 720, (asshown in FIG. 7B). In particular, the camera was configured to identifycontamination of the container's sealing region 722 with lunch meat 730before and during sealing of container 720 with seal 724. The imagingwas performed either using either a camera configured for visualspectrum imaging (here an industrial SONY 4 Mega pixel color camera) ora thermal camera configured for LWIR imaging (here a FLIR 640×512 pixelsuncooled bolometer). The obtained images were sent to a processorconfigured to retrieve one or more image frames and to process theimages to identify contaminations using suitable machine learningalgorithms for image processing, as essentially disclosed herein. Asshown in FIG. 8A, the hereindisclosed thermal image system enabled cleardistinction between container 720 and content (here lunch meat 730),this due to the temperature difference between the cold lunch meat(about 0-4° C.) as opposed to the room temperature (about 10-15° C.),thus ensuring a strong contrast for the thermal imaging. This enabledclear and certain identification of contaminations of sealing region722. Visual imaging (FIG. 8B), on the other hand, provided a much moreblurred image, making it difficult to distinguish between container 720and lunch meat 730.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” or “comprising”, whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, or components, but do notpreclude or rule out the presence or addition of one or more otherfeatures, integers, steps, operations, elements, components, or groupsthereof.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “estimating”, or the like, refer to theaction and/or processes of a computer or computing system, or similarelectronic computing device, that manipulate and/or transform datarepresented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

Embodiments of the present invention may include apparatuses forperforming the operations herein. This apparatus may be speciallyconstructed for the desired purposes, or it may comprise ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer readable storage medium, such as, but not limitedto, any type of disk including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs) electrically programmable read-only memories (EPROMs),electrically erasable and programmable read only memories (EEPROMs),magnetic or optical cards, or any other type of media suitable forstoring electronic instructions, and capable of being coupled to acomputer system bus.

The processes and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the desired method. The desired structure for avariety of these systems will appear from the description below. Inaddition, embodiments of the present invention are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the inventions as described herein.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, and so forth, whichperform particular tasks or implement particular abstract data types.The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

While a number of exemplary aspects and embodiments have been discussedabove, those of skill in the art will recognize certain modifications,additions and subcombinations thereof. It is therefore intended that thefollowing appended claims and claims hereafter introduced be interpretedto include all such modifications, additions, and sub-combinations asare within their true spirit and scope.

1. A method for improving packaging line sealing performance, the methodcomprising: obtaining a plurality of images of at least a part of asealing region of containers sealed at the packaging line, wherein theimagining is performed using at least one imaging camera operative at awavelength in the range of 0.9 μm-14 μm; and obtaining and/ordetermining at least one packaging line parameter, applying big dataanalysis on the plurality of images and on the at least one packagingline parameter, computing a trend in the sealing performance of thepackaging line based on the analysis, and providing an indicationregarding a detected and/or predicted sealing process deficiency whenthe trend is indicative of a decline in the sealing performance of thepackaging line.
 2. The method of claim 1, wherein the at least onepackaging line parameters comprise one or more sealing qualityparameters and/or one or more sealing efficiency parameters.
 3. Themethod of claim 2, wherein computing the trend further comprisesdifferentiating between sealing quality and sealing efficiency.
 4. Themethod according to claim 2, wherein the one or more sealing qualityparameters is selected from sealing temperature, sealing pressure,sealing time, laminate, supplier pressure applied on cutting jaws,customer complaints, flow of filling product, product weight, nozzleposition, heat of filing material, viscosity of filling material or anycombination thereof.
 5. The method according to claim 2, wherein the oneor more sealing efficiency parameters is selected from time betweensealer cleanups, duration of cleanups, statistics of Teflon replacementson sealing bars, time between packaging line stops, duration of thestops, number of sealed products per minute, speed of packing line, orany combination thereof.
 6. The method according to claim 1, furthercomprising extracting one or more sealing features from the plurality ofimages, wherein the sealing features are selected from sealing area,sealing length, sealing width, sealing disconnection, sealinguniformity, alignment, thermal radiation or any combination thereof, andwherein the big data analysis is further applied on the one or moresealing features.
 7. The method of claim 1, further comprising issuingan alert if the computed trend is indicative of a detected or predictedsealing inefficiency.
 8. The method of claim 7, further comprisingconducting a follow up inspection, wherein the follow up comprisestaking into account actions taken to overcome the detected or predictedsealing inefficiency.
 9. The method of claim 1, further comprisingobtaining a plurality of images captured during filling of the pluralityof containers, obtaining and/or determining one or more fillingparameters, applying big data filling analysis on the plurality ofimages captured during the filling and on the at least one filingparameter, and computing a trend in a filing performance of thepackaging line, based on the filling analysis.
 10. The method accordingto claim 1, wherein the container is selected from the group consistingof: a canister; a blister package, a tube, a heat seal bag, pouch,sachet, bottle, or any combination thereof.
 11. The method according toclaim 1, wherein the filling material is selected from the groupconsisting of: a liquid, a paste, a cream, a foam, or any combinationthereof.
 12. The method according to claim 1, wherein the fillingmaterial is a food stuff.
 13. The method according to claim 1, whereinthe sealing comprises heat sealing.
 14. A packaging system comprising: apackage line comprising at least a filling station for filling acontainer with a filling material and a sealing station for sealing ofthe container; and an imaging camera operative at a wavelength in therange of 0.01 μm-14 μm, the imaging camera positioned and configured toenable imaging of at least part of a sealing region of containerprocessed on the packaging line; and a processor configured to obtain aplurality of images of at least a part of a sealing region of thecontainers processed on the packaging line, wherein the imagining isperformed using at least one imaging camera operative at a wavelength inthe range of 0.9 μm-14 μm; and obtain and/or determine at least onepackaging line parameter, apply big data analysis on the plurality ofimages and on the at least one packaging line parameter, compute a trendin the sealing performance of the packaging line based on the analysis,and provide an indication regarding a detected and/or predicted sealingprocess deficiency when the trend is indicative of a decline in thesealing performance.
 15. The system according to claim 14, wherein theat least one packaging line parameters comprise one or more sealingquality parameters and/or one or more sealing efficiency parameters. 16.The system according to claim 15, wherein computing the trend furthercomprises differentiating between sealing quality and sealingefficiency.
 17. The system according to claim 15, wherein the one ormore sealing quality parameters is selected from sealing temperature,sealing pressure, sealing time, laminate, supplier pressure applied oncutting jaws, customer complaints, flow of filling product, productweight, nozzle position, heat of filing material, viscosity of fillingmaterial or any combination thereof.
 18. The system according to claim15, wherein the one or more sealing efficiency parameters is selectedfrom time between sealer cleanups, duration of cleanups, statistics ofTeflon replacements on sealing bars, time between packaging line stops,duration of the stops, number of sealed products per minute, speed ofpacking line, or any combination thereof.
 19. The system according toclaim 14, wherein the processor is further configured to extract one ormore sealing features from the plurality of images, wherein the sealingparameters are selected from sealing area, sealing length, sealingwidth, sealing disconnection, sealing uniformity, alignment, thermalradiation or any combination thereof, and wherein the big data analysisis further applied on the one or more sealing parameters.
 20. The systemof claim 14, wherein the processor is further configured to obtain aplurality of images captured during filling of the plurality ofcontainers, obtain and/or determine one or more filling parameters,apply big data filling analysis on the plurality of images capturedduring the filling and on the at least one filing parameter, andcomputing a trend in a filing performance of the packaging line, basedon the filling analysis.